@文章{info:doi/10.2196/37623，作者=“赵月华与朱，思诚与万，强与李，天一与邹，春与王，郝与邓，三红”，标题=“了解新冠肺炎虚假信息是如何在社交媒体上传播的以及由谁传播的:编码与网络分析”，期刊=“J医学互联网研究”，年=“2022”，月=“君”，日=“20”，卷=“24”，号=“6”，页=“e37623”，关键词=“健康虚假信息;COVID-19;社交媒体;错误的信息传播;infodemiology;全球健康危机;错误信息;理论模型;医学信息;流行; pandemic", abstract="Background: During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. Objective: We propose an elaboration likelihood model--based theoretical model to understand the persuasion process of COVID-19--related misinformation on social media. Methods: The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19--related misinformation feature includes five topics: medical information, social issues and people's livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic--related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. Results: Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80{\%}). Moreover, the results suggest that both the least (4660/11,301, 41.24{\%}) and most (2320/11,301, 20.53{\%}) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00{\%} (2364/2437) of the spread was characterized by radiation dissemination. Conclusions: Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics. ", issn="1438-8871", doi="10.2196/37623", url="//www.mybigtv.com/2022/6/e37623", url="https://doi.org/10.2196/37623", url="http://www.ncbi.nlm.nih.gov/pubmed/35671411" }